What Is The Max Token Limit In OpenAI ChatGPT

The Max Token Limit In ChatGPT: GPT-4o (128,000 tokens), GPT-4 Turbo (128,000 tokens), GPT-4V (128,000 tokens), GPT-4 (8,192 tokens).


ModelMax TokensTraining Data
GPT-4o128,000 tokensUp to Dec 2023
GPT-4 Turbo with Vision128,000 tokensUp to Dec 2023
gpt-4-turbo-2024-04-09128,000 tokensUp to Dec 2023
gpt-4-0125-preview128,000 tokensUp to Dec 2023
gpt-4-turbo-preview128,000 tokensUp to Dec 2023
gpt-4-1106-preview128,000 tokensUp to Apr 2023
gpt-4-vision-preview128,000 tokensUp to Apr 2023
gpt-4-1106-vision-preview128,000 tokensUp to Apr 2023
gpt-48,192 tokensUp to Sep 2021
gpt-4-06138,192 tokensUp to Sep 2021
gpt-4-32k32,768 tokensUp to Sep 2021
gpt-4-32k-061332,768 tokensUp to Sep 2021
gpt-3.5-turbo-012516,385 tokensUp to Sep 2021
gpt-3.5-turbo-110616,385 tokensUp to Sep 2021
gpt-3.5-turbo4,096 tokensUp to Sep 2021
gpt-3.5-turbo-16k16,384 tokensUp to Sep 2021
gpt-3.5-turbo-instruct4,096 tokensUp to Sep 2021
gpt-3.5-turbo-06134,096 tokensUp to Sep 2021
gpt-3.5-turbo-16k-061316,384 tokensUp to Sep 2021
gpt-3.5-turbo-03014,096 tokensUp to Sep 2021
Last updated: Apr 10, 2024

This article aims to illuminate an integral yet less-explored facet of AI language models, the token limit. The goal is to equip readers with a thorough understanding of token limits in the context of AI language models and their significance in the continued development and utilization of these advanced tools.

What Is Token

In the context of natural language processing (NLP) and language models, a “token” represents the most fundamental unit of data that the model is designed to handle. A token can be as small as a single character or as large as a word, depending on the specifics of the language and the model’s design.

In AI language models like GPT-4, a token often corresponds to a single word, but it can also represent a part of a word, a whole phrase, or even punctuation or whitespace. For example, in the sentence “ChatGPT is an AI model,” there would be five tokens: “ChatGPT,” “is,” “an,” “AI,” “model,” and the punctuation “.”.

In essence, tokens are the “building blocks” that language models use to understand and generate text. They form the basis of the input and output data, and the quantity, variety, and quality of tokens directly influence the effectiveness of the model’s performance.

Why There is a Token Limit in GPT Models

OpenAI’s GPT Models operate on a token limit due to several reasons that revolve around efficiency, computational feasibility, and model performance:

  • Computational Efficiency: Handling vast amounts of tokens concurrently demands significant computing resources, including memory and processing power. Setting a token limit helps manage the computational cost and ensures the language model operates within reasonable timeframes, providing timely responses.
  • Model Performance: A token limit helps maintain the quality of output. As language models like GPT-3 or GPT-4 generate responses based on the context of previous tokens, the more tokens it processes, the higher the chance for the model to lose track of the initial context, potentially affecting the coherence of the generated text.
  • Memory Limitations: The token limit is inherently tied to the architecture of the neural networks used in language models. For instance, transformer-based models like GPT-3 and GPT-4 have a fixed-size attention window due to their architecture. This determines how many tokens the model can ‘remember’ or pay attention to at once.
  • Resource Allocation: Setting a token limit helps balance resource usage among multiple simultaneous users, ensuring fair access to computational resources in a multi-user environment.

Token Limit in GPT-4

Latest modelDescriptionMax tokensTraining data
GPT-4oThe most advanced, multimodal flagship model that’s cheaper and faster than GPT-4 Turbo. Currently points to gpt-4o-2024-05-13.128,000Up to Dec 2023
gpt-4o-2024-05-13gpt-4o currently points to this version.128,000Up to Dec 2023
gpt-4-turboThe latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Currently points to gpt-4-turbo-2024-04-09.128,000Up to Dec 2023
gpt-4-turbo-2024-04-09GPT-4 Turbo with Vision model. Vision requests can now use JSON mode and function calling. gpt-4-turbo currently points to this version.128,000Up to Dec 2023
gpt-4-0125-previewGPT-4 Turbo preview model intended to reduce cases of “laziness” where the model doesn’t complete a task.128,000Up to Dec 2023
gpt-4-turbo-previewCurrently points to gpt-4-0125-preview.128,000Up to Apr 2023
gpt-4-1106-previewGPT-4 Turbo model featuring improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens. This preview model is not yet suited for production traffic.128,000Up to Apr 2023
gpt-4-vision-previewCurrently points to gpt-4-1106-vision-preview.128,000Up to Apr 2023
gpt-4-1106-vision-previewGPT-4 with the ability to understand images, in addition to all other GPT-4 Turbo capabilities.128,000Up to Apr 2023
gpt-4More capable than any GPT-3.5 model, able to do more complex tasks, and optimized for chat.8,192Up to Sep 2021
gpt-4-0613Snapshot of gpt-4 from June 13th 2023 with function calling data. Unlike gpt-4, this model will not receive updates, and will be deprecated 3 months after a new version is released.8,192Up to Sep 2021
gpt-4-32kSame capabilities as the base gpt-4 mode but with 4x the context length.32,768Up to Sep 2021
gpt-4-32k-0613Snapshot of gpt-4-32 from June 13th 2023. Unlike gpt-4-32k, this model will not receive updates, and will be deprecated 3 months after a new version is released.32,768Up to Sep 2021

Token Limit in GPT-3.5

Latest modelDescriptionMax tokensTraining data
gpt-3.5-turbo-0125The latest GPT-3.5 Turbo model with higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls.16,385 tokensUp to Sep 2021
gpt-3.5-turbo-1106GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. 16,385 tokensUp to Sep 2021
gpt-3.5-turboMost capable GPT-3.5 model and optimized for chat at 1/10th the cost of text-davinci-003.4,096 tokensUp to Sep 2021
gpt-3.5-turbo-16kSame capabilities as the standard gpt-3.5-turbo model but with 4 times the context.16,385 tokensUp to Sep 2021
gpt-3.5-turbo-instructSimilar capabilities as text-davinci-003 but compatible with legacy Completions endpoint and not Chat Completions.4,096 tokensUp to Sep 2021
gpt-3.5-turbo-0613Snapshot of gpt-3.5-turbo from June 13th 2023 with function calling data. Unlike gpt-3.5-turbo, this model will not receive updates, and will be deprecated on June 13, 2024.4,096 tokensUp to Sep 2021
gpt-3.5-turbo-16k-0613Snapshot of gpt-3.5-turbo-16k from June 13th 2023. Unlike gpt-3.5-turbo-16k, this model will not receive updates, and will be deprecated on June 13, 2024.16,385 tokensUp to Sep 2021
gpt-3.5-turbo-0301Snapshot of gpt-3.5-turbo from March 1st 2023. Will be deprecated on June 13th 2024.4,096 tokensUp to Sep 2021

Embeddings Models

ModelDescriptionOutput Dimension
text-embedding-3-largeMost capable embedding model for both english and non-english tasks.3,072
text-embedding-3-smallIncreased performance over 2nd generation ada embedding model1,536
text-embedding-ada-002Most capable 2nd generation embedding model, replacing 16 first generation models.1,536

Token Limt In Moderation Models

ModelDescriptionMax tokens
text-moderation-latestCurrently points to text-moderation-007.32,768
text-moderation-stableCurrently points to text-moderation-007.32,768
text-moderation-007Most capable moderation model across all categories.32,768

Token Limt In GPT Base Models

ModelDescriptionMax tokensTraining Data
babbage-002Replacement for the GPT-3 ada and babbage base models.16,384Up to Sep 2021
davinci-002Replacement for the GPT-3 curie and davinci base models.16,384Up to Sep 2021

How Token Limit Can Affect the Utility of ChatGPT

While the token limit is necessary for practical and computational reasons, it does pose constraints on the utility of ChatGPT, affecting its applicability in various scenarios. Understanding these constraints can help in designing applications and interfaces that effectively work within these limits while still delivering valuable results.

  • Conversation Length: The most direct impact of the token limit is on the length of the conversations that can be handled. Both the input and the output count towards the token limit, meaning longer conversations may not fit within the limit, necessitating the trimming or omission of some parts.
  • Contextual Understanding: Since the token limit also defines the ‘memory’ of a language model, it can influence the model’s contextual understanding. If a conversation exceeds the token limit, the model may lose the earlier parts of the context, which could lead to less relevant or coherent responses.
  • Comprehensive Responses: The token limit can also constrain the model’s ability to provide more comprehensive, elaborate responses. For example, if the token limit is close to being reached, the model will have to generate shorter responses, potentially reducing the depth or detail of the information provided.
  • Multi-Turn Conversations: For dialogues involving many back-and-forths or multiple participants, the token limit can become a critical factor. The conversation needs to fit within the model’s token limit, which might be challenging with many conversational turns.
  • Real-Time Interactions: In real-time applications where rapid responses are needed, the time taken to process a large number of tokens can become significant, affecting the user experience.

What The Differences Between Rate Limits And Token Limits

Rate limits restrict the number of API requests. Token limits restrict the number of tokens (usually words) sent to a model per request. For example, gpt-4-32k-0613 has a max of 32,768 tokens per request. You can’t increase the token limit, only reduce the number of tokens per request.

See Also: What Are The Rate Limits For OpenAI API?

How to Get Around OpenAI Token Limits

Despite the necessity of token limits in GPT models, developers have identified several strategies to manage and overcome these constraints, ensuring efficient and effective use of ChatGPT:

  • Condensing Input: Developers can pre-process and condense inputs to the model, summarizing information or removing unnecessary details. This helps in preserving the most important context within the token limit.
  • Truncation: In cases where the input exceeds the token limit, developers can truncate the text to fit within the limit. While this might result in the loss of some information, careful truncation can help maintain the essential context.
  • Continuation: If a conversation or text analysis task exceeds the token limit, it can be broken down into smaller parts, each processed separately and in sequence. This can be helpful for tasks like document analysis.
  • Optimized Model Design: Researchers are constantly working on designing more optimized models and techniques to handle token limits better, such as Sparse Transformers that can handle more tokens within the same computational constraints.
  • Prompt Engineering: Careful crafting of the model’s prompt can help elicit more concise and to-the-point responses, conserving tokens for further conversation.
  • Model Customization: When possible, advanced users can potentially adjust the model’s parameters to better manage the token limit, such as tweaking the ‘temperature’ parameter to control the randomness of output and the length of the responses.

See Also: Overcome OpenAI API Token Limits In GPT-4/3.5 Models With lightspeedGPT

The Future of Token Limits in GPT Models

The future of token limits in GPT models is a dynamic field, intertwined with the broader trajectory of AI research and technological advancements. Here are a few possibilities and directions the future might hold:

  • Higher Token Limits: As computational power increases and model architectures become more efficient, future GPT models might feature higher token limits, allowing for longer conversations and more complex tasks to be handled.
  • Improved Handling of Tokens: Innovations in AI could lead to more effective token management, such as improved contextual understanding over long token sequences or more efficient handling of tokens within existing computational constraints.
  • Beyond the Token Concept: Future research might evolve beyond the concept of tokens altogether. New paradigms, such as models based on byte or character-level processing or even entirely novel concepts, could revolutionize how language models process text.
  • Personalized Token Limits: We might see the development of personalized token limits, dynamically adapting based on the task’s nature or the available computational resources.
  • Balancing Act: The future of token limits will likely continue to be a balancing act between computational feasibility, model performance, and practical utility. How this balance is struck might vary based on the use case, user requirements, and technological advancements.

Please note that as AI research advances, the community’s understanding of token limits and their implications will continue to evolve, shaping the future development and use of GPT models. As of now, we can only speculate on these possibilities, with the actual future likely to bring surprises and innovations beyond our current anticipation.

See Also:




  • Updated for GPT-4o

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